The promise of Method 2 for online application for low-level data selection further motivates the use of smaller networks, and, for the purposes of further acceleration on FPGA, smaller input images. The second method was therefore further explored for a number of other customizable networks, besides VGG16b [1]}, including a smaller, simpler CNN than VGG16b, referred to as CNN_s [2]}, a Multi-Layer Perceptron (MLP) network [3]}, and a ResNet14b network [4]}. Results from the three additional networks are provided in Tabs. REF  through REF , to be considered in comparison with VGG16b results in Table REF . The best performance is obtained with VGG16b and CNN_s. The simple CNN (CNN_s) performs comparably with VGG16b in terms of the accuracy, albeit with with slightly higher pass rate (\(\sim \) 0.5%) on background noise ROIs. MLP and ResNet14b also have comparable pass rates (\(\sim \) 0.5%) for background noise, but the accuracies for low energy and high energy ROIs are not as high as those for VGG16b or CNN_s. Inference times with CNN_s (on a single GPU card) are an order of magnitude lower than for VGG16b, due to the reduced number of layers and convolutions per layer.
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